---

name: cleanlab description: "Routes cleanlab data-centric AI workflows for finding label errors, dataset issues, outliers, annotator quality, and task-specific label-quality problems." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

cleanlab

Use this repo skill when a task involves the cleanlab Python package or the broader cleanlab workflow vocabulary: data-centric AI, label errors, noisy labels, label quality, dataset health, Datalab audits, CleanLearning, outliers, near duplicates, non-IID checks, annotator quality, or task-specific label issue detection.

cleanlab is API-first. It does not expose a package-specific command-line interface in this checkout; route future agents to Python APIs and the bundled smoke scripts.

Install and quick verification

For normal use:

python -m pip install cleanlab

For Datalab or image issue workflows:

python -m pip install "cleanlab[datalab]"
python -m pip install "cleanlab[image]"
# or all stable optional extras:
python -m pip install "cleanlab[all]"

For a local checkout when developing or validating this exact repo version:

python -m pip install -e ".[all]"

Then run the bundled root check when you need a safe import smoke test:

python scripts/check_install.py --include-optional

Read references/troubleshooting.md if installation, optional dependencies, imports, model probabilities, or shape validation fail. Read references/repo-provenance.md before deciding whether this skill is stale for another checkout.

Route map

Task signal	Read this sub-skill	Why
Broad audit of one dataset; Datalab, find_issues, report, get_issues, issue summaries, custom issue managers, image issue checks	sub-skills/datalab/SKILL.md	Datalab orchestrates multiple issue families and report tables.
Standard single-label binary/multiclass noisy-label cleanup; CleanLearning; cleanlab.filter, count, rank, dataset; data valuation; synthetic noise	sub-skills/classification/SKILL.md	Main direct API route for multiclass label quality and robust learning.
Multiple annotators, consensus labels, annotator quality, active learning / relabeling priorities	sub-skills/multiannotator/SKILL.md	Multiannotator APIs combine raw annotations with model probabilities.
Outlier or OOD scoring from feature embeddings or pred_probs alone	sub-skills/outlier/SKILL.md	OutOfDistribution returns score vectors for atypical examples.
Multi-label classification labels as list-of-lists; regression target label issues	sub-skills/tabular-label-issues/SKILL.md	Stable nonstandard tabular label workflows with distinct formats.
Token classification, object detection, semantic segmentation label issues, structured outputs, bounding boxes, masks	sub-skills/structured-label-issues/SKILL.md	Nested token lists, object boxes, and pixel masks need task-specific APIs.
Explicit cleanlab.experimental use, low-memory batched helper, span classification wrapper, PyTorch/CIFAR/MNIST/co-teaching examples	sub-skills/experimental/SKILL.md	Experimental helpers are unstable or optional; prefer stable routes first.

Operating rules

• Prefer out-of-sample model probabilities. For label-error workflows, pred_probs should be aligned to the same rows as labels and should come from held-out data, cross-validation, or a model trained elsewhere.
• Validate shapes before running expensive workflows: classification (N, K) probabilities, multilabel (N, K) independent probabilities, token lists per sentence, object-detection box arrays, and segmentation (N, K, H, W) probabilities.
• Use Datalab for a broad issue audit and direct module APIs for focused label-quality or scoring tasks.
• Treat experimental helpers as opt-in. Do not install torch, torchvision, or skorch unless the user explicitly chooses those optional deep-learning examples.
• If a workflow needs image-specific Datalab checks, install the image extra or all extras and verify cleanvision imports.
• The generated references and scripts are self-contained. Do not instruct future agents to open this repo's original docs, tests, notebooks, or source scripts for normal operation.

Shared runtime files

• Read references/troubleshooting.md for cross-cutting package install/import, optional dependency, API input, and stale-version recovery guidance.
• Read references/repo-provenance.md to compare a future checkout against the commit and evidence used to generate this skill.
• references/repo-routing-metadata.json is structured metadata consumed by DisCo's repo-skills router import transaction.
• Run scripts/check_install.py to verify the installed package, stable submodules, and optional extras before using deeper sub-skills.

Good first branch decisions

• If the prompt says "find all issues in my dataset" or mentions lab.report(), start with Datalab.
• If it says "mislabeled examples" for a normal classifier and already has pred_probs, start with classification.
• If it says "bad annotators", "consensus", or "which examples should be relabeled", start with multiannotator.
• If it says "OOD", "outlier score", or "feature embeddings look atypical", start with outlier unless the user also wants a broad Datalab audit.
• If it says "multilabel", "regression target issue", or "numeric labels look wrong", start with tabular label issues.
• If it says "token classification", "NER", "bounding boxes", "object detection", "semantic segmentation", or "pixel masks", start with structured label issues.